Enterprise Analytics: Optimize Performance, Process and Decisions Through Big Data

Davenport, Thomas H.

ISBN 10: 0133039439 / ISBN 13: 9780133039436

Published by Ft Pr, 2012

About this Item

Reading copy. May have signs of wear and previous use scuffs, library copy, highlighting, writing, and underlining . Dust jacket may be missing. 100% GUARANTEE! Shipped with delivery confirmation, if youâ re not satisfied with purchase.Ships USPS Media Mail. Seller Inventory # OTV.0133039439.A

Bibliographic Details

Title: Enterprise Analytics: Optimize Performance, ...

Publisher: Ft Pr

Publication Date: 2012

Binding: Hardcover

Condition: acceptable

About this title

Synopsis:

The Definitive Guide to Enterprise-Level Analytics Strategy, Technology, Implementation, and Management

Organizations are capturing exponentially larger amounts of data than ever, and now they have to figure out what to do with it. Using analytics, you can harness this data, discover hidden patterns, and use this knowledge to act meaningfully for competitive advantage. Suddenly, you can go beyond understanding “how, when, and where” events have occurred, to understand why – and use this knowledge to reshape the future. Now, analytics pioneer Tom Davenport and the world-renowned experts at the International Institute for Analytics (IIA) have brought together the latest techniques, best practices, and research on analytics in a single primer for maximizing the value of enterprise data. Enterprise Analytics is today’s definitive guide to analytics strategy, planning, organization, implementation, and usage. It covers everything from building better analytics organizations to gathering data; implementing predictive analytics to linking analysis with organizational performance. The authors offer specific insights for optimizing supply chains, online services, marketing, fraud detection, and many other business functions. They support their powerful techniques with many real-world examples, including chapter-length case studies from healthcare, retail, and financial services. Enterprise Analytics will be an invaluable resource for every business and technical professional who wants to make better data-driven decisions: operations, supply chain, and product managers; product, financial, and marketing analysts; CIOs and other IT leaders; data, web, and data warehouse specialists, and many others.

From the Back Cover:

The Definitive Guide to Enterprise-Level Analytics Strategy, Technology, Implementation, and Management

· From the International Institute for Analytics, the world’s premier analytics research organization
· Proven best practices for choosing projects and technology, building analytics organizations, and measuring ROI
· Brand-new case studies from finance, healthcare, technology, retail, manufacturing, and beyond

As businesses recognize the immense potential of analytics, they are increasingly treating it as an enterprise resource and an enterprise priority. Now, the world’s leading researchers on large-scale analytics have brought together today’s best practices for planning, implementing, managing, and succeeding with analytics on an enterprise level.

Drawing on their pioneering work at the International Institute for Analytics, the authors illuminate the entire analytics lifecycle, addressing technology, applications, operations, governance, strategy, and more. They offer actionable solutions for leveraging proprietary data, gaining a new 360-degree view of the customer, measuring engagement in actionable ways, optimizing “next best offers,” applying analytics at production scale, using cloud resources, and linking analytics directly to business performance. Then, in a full section on the crucial human side of analytics, they address everything from building and governing analytics organizations to integrating analytics throughout global businesses.

Whether you’re an analyst, manager, strategist, executive, or technology vendor, this book will help you implement large-scale analytics faster, avoid crucial missteps, and capture far more value.